update

Signed-off-by: Vladimir Mandic <mandic00@live.com>

README.md

@@ -6,20 +6,32 @@ library_name: diffusers
 
 # MicroDecoder
 
-**Micro-VAE** that can be trained on *any* diffusion model in **~15min** and provides low-quality reconstruction from latents to RGB in **~0.01sec**.  
-Inference includes noise correction based on current timestep, intentional blurring plus upscale interpolation: all with intention of providing as fast-as-possible reconstruction that is viable and consistent with any noise levels.  
+**MicroDecoder** VAE can be trained on *any* diffusion model in **~15min** and provides low-quality reconstruction from latents to RGB in **~0.01sec**.  
+
+Inference includes noise correction based on current timestep, blurring corrections plus upscale interpolation: all with intention of providing as fast-as-possible reconstruction that is viable and consistent with any noise levels.  
+
 Intended use-case is live-preview during generative model inference.  
 
 Shapes/Channels/etc are inferred from the base VAE, so no configuration changes are needed between different models.
 
-- Model definition and training code [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro_train.py)
+## Models
+
+Models included in this repo are pre-trained for following architectures:
+- **SD, SDXL, Flux.1, Flux.2, Qwen, Qwen-21, Wan-21, MiniMax-H3**
+
+## Code
+
+- Model definition [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro_model.py)
+- Training code [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro_train.py)
 - Example inference code [here](https://github.com/vladmandic/sdnext/blob/dev/modules/vae/sd_vae_micro.py)
 
+## Example
+
 Example using **MicroDecoder** with `Flux.2-Klein-9B` and compared with official final VAE processing at the end:
 
 ![MicroDecoder](https://huggingface.co/vladmandic/MicroDecoder/resolve/main/MicroDecoder.jpg)
 
-## Example
+## Training
 
 ```shell
 sd_vae_micro_train.py \
@@ -35,18 +47,18 @@ sd_vae_micro_train.py \
 ```
 
 ```log
-Encoding Latents: 100%
-Training MicroDecoder [350 epochs | Channels: 32 | Hidden: 256 | Scale: 4x]
-Dataset: 360 train samples | 40 validation samples | EMA decay: 0.999
-
-Epoch  | Train Tot | Tr PSNR | Tr SSIM | Val Tot  | Val PSNR | Val SSIM | Val L1  | Val LAB
---------------------------------------------------------------------------------------------
-001/350  | 5.7244    | 10.38   | 0.3431  | 5.9729   | 10.56    | 0.3047   | 0.2676  | 0.4254  *
-100/350  | 2.7690    | 25.49   | 0.8030  | 2.2011   | 26.52    | 0.8672   | 0.0376  | 0.0699  *
-200/350  | 2.4006    | 27.04   | 0.8400  | 1.1679   | 34.24    | 0.9635   | 0.0132  | 0.0317  *
-300/350  | 2.3950    | 28.05   | 0.8472  | 1.0002   | 35.63    | 0.9723   | 0.0113  | 0.0282  *
-350/350  | 2.3010    | 28.51   | 0.8511  | 0.9781   | 35.83    | 0.9734   | 0.0111  | 0.0278  *
-
-Training complete! Best validation PSNR: 35.83 dB (Epoch 350)
-Saved best EMA model weights to: MicroVAE-qwen21.safetensors
+MicroDecoder Train
+Args: Namespace(folder='/home/vlado/generative/Input/vae/', max=500, resolution=512, crop='center', vae='AutoencoderKLMiniMaxH3', repo='OzzyGT/MiniMax_H3_sdnq_dynamic_4bit', subfolder='vae', dim=256, epochs=400, scale=4, batch=16, lr=0.0003, ema=0.999, val=0.1, output='microdecoder-minimaxh3.safetensors', device='cuda')
+Base VAE: cls=<class 'diffusers.models.autoencoders.autoencoder_kl_minimax_h3.AutoencoderKLMiniMaxH3'> repo="OzzyGT/MiniMax_H3_sdnq_dynamic_4bit" subfolder="vae"
+Init VAE: model=MicroDecoder ema=EMAModel optimizer=AdamW scheduler=CosineAnnealingLR criterion=EnhancedQualityLoss metrics=DetailedMetricsTracker
+Epoch   | tTotal    | tPSNR   | tSSIM   | vTotal   | vPSNR    | vSSIM    | vL1     | vLAB
+-------------------------------------------------------------------------------------------
+001/400 | 7.0891    | 10.49   | 0.2540  | 7.1642   | 10.75    | 0.2357   | 0.2614  | 0.4146
+...
+400/400 | 4.5365    | 21.03   | 0.6402  | 1.9644   | 30.61    | 0.9266   | 0.0204  | 0.0465
+Load   ━━━━━━━━━━━━━━━━━━━━ 400/400 100% 0:00:00 0:00:08 Images=400 Shape=torch.Size([400, 3, 512, 512])
+Encode ━━━━━━━━━━━━━━━━━━━━ 400/400 100% 0:00:00 0:00:46 Train=360 Validation=40 EMA=0.999
+Train  ━━━━━━━━━━━━━━━━━━━━ 400/400 100% 0:00:00 0:06:33 Train(psnr=21.029 ssim=0.640 lpips=2.784 l1=0.089 lab=0.156 grad=0.073 sat=0.124 fft=0.071 total=4.536) Validate(psnr=30.607 ssim=0.927 lpips=1.242 l1=0.020 lab=0.046 grad=0.046 sat=0.057 fft=0.032 total=1.964)
+Complete: Time=393.89 Epoch/Sec=1.02 PSNR=30.61 dB @ Epoch=400
+Save: filename="microdecoder-minimaxh3.safetensors"
 ```

microdecoder-minimaxh3.safetensors

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